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At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Quantifying the role of moss in terrestrial ecosystem carbon dynamics in northern high latitudes

Mosses are ubiquitous in northern terrestrial ecosystems, and play an important role in regional carbon, water and energy cycling. Current global land surface models that do not consider mosses may bias the quantification of regional carbon dynamics. Here we incorporate mosses as a new plant functional type into the process-based Terrestrial Ecosystem Model (TEM 5.0), to develop a new model (TEM_Moss). The new model explicitly quantifies the interactions between vascular plants and mosses and their competition for energy, water, and nutrients. Compared to the estimates using TEM 5.0, the new model estimates that the regional terrestrial soils currently store 132.7 Pg more C and will store 157.5 and 179.1 Pg more C under the RCP8.5 and RCP2.6 scenarios, respectively, by the end of the 21st century. Ensemble regional simulations forced with different parameters for the 21st century with TEM_Moss predict that the region will accumulate 161.1±142.1 Pg C under the RCP2.6 scenario and 186.7±166.1 Pg C under the RCP8.5 scenario over the century. Our study highlights the necessity of coupling moss into Earth system models to adequately quantify terrestrial carbon–climate feedbacks in the Arctic.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Modest net autotrophy in the oligotrophic ocean

The metabolic state of the oligotrophic subtropical ocean has long been debated. Net community production (NCP) represents the balance of autotrophic carbon fixation with heterotrophic respiration. Many in vitro NCP estimates based on oxygen incubation methods and the corresponding scaling relationships used to predict the ecosystem metabolic balance have suggested the ocean gyres to be net heterotrophic; however, all in situ NCP methods find net autotrophy. Reconciling net heterotrophy requires significant allochthonous inputs of organic carbon to the oligotrophic gyres to sustain a preponderance of respiration over in situ production. Here we use the first global ecosystem-ocean circulation model that contains representation of the three allochthonous carbon sources to the open ocean, to show that the five oligotrophic gyres exhibit modest net autotrophy throughout the seasonal cycle. Annually integrated rates of NCP vary in the range ~1.5–2.2 mol O 2 m -2 yr -1 across the five gyre systems; however, seasonal NCP rates are as low as ~1 ± 0.5 mmol O2 m -2 d -1 for the North Atlantic. Volumetric NCP rates are heterotrophic below the 10% light level; however, they become net autotrophic when integrated over the euphotic zone. Observational uncertainties when measuring these modest autotrophic NCP rates as well as the metabolic diversity encountered across space and time complicate the scaling up of in vitro measurements to the ecosystem scale and may partially explain the previous reports of net heterotrophy. The oligotrophic ocean is autotrophic at present; however, it could shift toward seasonal heterotrophy in the future as rising temperatures stimulate respiration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Final Report for Project "A high-throughput pipeline for mapping inter-species interactions and metabolic synergy relevant to next-generation biofuel production"

The goal of our project was to implement a pipeline for the systematic, computationally-driven study and optimization of microbial interactions and their effect on lignocellulose degradation and biofuel production. We specifically sought to design and construct artificial microbial consortia that could collectively degrade lignocellulose from plant biomass, and produce precursors of energy-rich biofuels. This project fits into the bigger picture goal of helping identify a sustainable strategy for the production of energy-rich biofuels that would satisfy the existing energy constraints and demand of our society. Based on the observation that complex natural microbial communities tend to be metabolically efficient and ecologically robust, we pursued the study of a microbial system in which the desired engineering function is achieved through division of labor across multiple microbial species. Our approach was aimed at bypassing the complexity of natural communities by establishing a rational approach to design small synthetic microbial consortia. Towards this goal, we combined multiple approaches, including computer modeling of ecosystem-level microbial metabolism, mass spectrometry of metabolites, genetic engineering, and experimental evolution. The microbial production of biofuels from lignocellulose is a complex, multi-step process. Microbial consortia are an ideal approach to consolidated bioprocessing: a community of microorganisms performs a wide variety of functions more efficiently and is more resilient to environmental perturbations than a microbial monoculture. Each organism we chose for this project addresses a specific challenge: lignin degradation (Pseudomonas putida); (hemi)cellulose degradation (Cellulomonas fimi); lignin degradation product demethoxylation (Methylobacterium spp); generation of biofuel lipid precursors (Yarrowia lipolytica). These organisms are genetically tractable, aerobic, and have been used in biotechnological applications. Throughout the project, we have used mass spectrometry to characterize and measure the metabolic inputs and outputs of each of these consortium members, providing valuable information for model refinement, and enabling the establishment of metabolism-mediated interactions. In addition to lignocellulose degradation, we have started addressing the challenge of removing metabolites (e.g. formaldehyde) produced by the demethoxylation of lignin monomers, which can otherwise inhibit microbial growth due to their toxicity. On the computational side, we have implemented genome-scale models for all consortium members, based on KBase reconstructions and literature curation, and we studied small consortia and their properties. Overall, our project has identified a complex landscape of interactions types and metabolic processes relevant to community-level functions, illustrating the challenges and opportunities of microbial community engineering for the transformation of biomass into bioproducts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Ecological stoichiometry as a foundation for omics-enabled biogeochemical models of soil organic matter decomposition

Coupled biogeochemical cycles drive ecosystem ecology by influencing individual-to-community scale behaviors; yet the development of process-based models that accurately capture these dynamics remains elusive. Soil organic matter (SOM) decomposition in particular is influenced by resource stoichiometry that dictates microbial nutrient acquisition (‘ecological stoichiometry’). Despite its basis in biogeochemical modeling, ecological stoichiometry is only implicitly considered in high-resolution microbial investigations and the metabolic models they inform. State-of-science SOM decomposition models in both fields have advanced largely separately, but they agree on a need to move beyond seminal pool-based models. This presents an opportunity and a challenge to maximize the strengths of various models across different scales and environmental contexts. To address this challenge, we contend that ecological stoichiometry provides a framework for merging biogeochemical and microbiological models, as both explicitly consider substrate chemistries that are the basis of ecological stoichiometry as applied to SOM decomposition. We highlight two gaps that limit our understanding of SOM decomposition: (1) understanding how individual microorganisms alter metabolic strategies in response to substrate stoichiometry and (2) translating this knowledge to the scale of biogeochemical models. We suggest iterative information exchange to refine the objectives of high-resolution investigations and to specify limited dynamics for representation in large-scale models, resulting in a new class of omics-enabled biogeochemical models. Assimilating theoretical and modelling frameworks from different scientific domains is the next frontier in SOM decomposition modelling; advancing technologies in the context of stoichiometric theory provides a consistent framework for interpreting molecular data, and further distilling this information into tractable SOM decomposition models.

Carbon cycling↗
DOE OSTI2015

Modeling forest carbon cycle response to tree mortality: Effects of plant functional type and disturbance intensity

Natural and anthropogenic disturbances influence ecological succession and impact the carbon cycle. Understanding disturbance effects and ecosystem recovery is essential to carbon modeling. Here, we hypothesized that (1) species-specific disturbances impact the carbon cycle differently from nonspecific disturbances. In particular, disturbances that target early-successional species will lead to higher carbon uptake by the postrecovery, middle- and late-successional community and (2) disturbances that affect the midsuccessional deciduous species have more intense and long-lasting impacts on carbon uptake than disturbances of similar intensity that only affect the early-successional species. To test these hypotheses, we employed a series of simulations conducted with the Ecosystem Demography model version 2 to evaluate the sensitivity of a temperate mixed-deciduous forest to disturbance intensity and type. Our simulation scenarios included a control (undisturbed) case, a uniform disturbance case where we removed 30% of all trees regardless of their successional status, five cases where only early-successional deciduous trees were removed with increasing disturbance intensity (30%, 70%, 85%, and 100%), and four cases of midsuccessional disturbances with increasing intensity (70%, 85%, and 100%). Our results indicate that disturbances affecting the midsuccessional deciduous trees led to larger decreases in carbon uptake as well as longer recovery times when compared to disturbances that exclusively targeted the early-successional deciduous trees at comparable intensities. Moreover, disturbances affecting 30% to 100% of early-successional deciduous trees resulted in an increased carbon uptake, beginning 6 years after the disturbance and sustained through the end of the 100 year simulation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Multi-scale assessment of a grassland productivity model

Grasslands provide many important ecosystem services globally, and projecting grassland productivity in the coming decades will provide valuable information to land managers. Productivity models can be well calibrated at local scales but generally have some maximum spatial scale in which they perform well. Here we evaluate a grassland productivity model to find the optimal spatial scale for parameterization and thus for subsequently applying it in future productivity projections for North America. We also evaluated the model on new vegetation types to ascertain its potential generality. We find the model most suitable when incorporating only grasslands, as opposed to also including agriculture and shrublands, and only in the Great Plains and eastern temperate forest ecoregions of North America. The model was not well suited to grasslands in North American deserts or northwest forest ecoregions. It also performed poorly in agriculture vegetation, likely due to management activities, and shrubland vegetation, likely because the model lacks representation of deep water pools. This work allows us to perform long-term projections in areas where model performance has been verified, with gaps filled in by future modeling efforts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Climate change and the future of natural disturbances in the central hardwood region

The spatial patterns and ecological processes of the southeastern upland hardwood forests have evolved to reflect past climatic conditions and natural disturbance regimes. Changes in climate can lead to disturbances that exceed their natural range of variation, and the impacts of these changes will depend on the vulnerability or resiliency of these ecosystems. Global Circulation Models generally project annual increases in temperature across the southeastern United States over the coming decades, but changes in precipitation are less consistent. Even more unclear is how climate change might affect future trends in the severity and frequency of natural disturbances, such as severe storms, fires, droughts, floods, and insect outbreaks. Here, we use a time-series satellite data record to map the spatial pattern and severity of broad classes of natural disturbances the southeast region. The data derived from this map allow analysis of regional-scale trends in natural and anthropogenic disturbances in the region over the last three decades. Throughout the region, between 5% and 25% of forest land is affected by some sort of disturbance each year since 1985. The time series reveals periodic droughts that themselves are widespread and of low severity but are associated with more localized, high-severity disturbances such as fire and insect outbreaks. The map also reveals extensive anthropogenic disturbance across the region in the form of forest conversion related to resource extraction and urban and residential development. We discuss how changes in climate and disturbance regimes might affect southeastern forests in the future via altering the exposure, sensitivity and adaptive capacity of these ecosystems. Changes in climate are highly likely to expose southeastern forests to more frequent and severe disturbances, but ultimately how vulnerable or resilient southeastern forests are to these changes will depend on their sensitivity and capacity to adapt to these novel conditions.

Dale, Virginia H↗
NASA NTRS2020

The “Gearamid” Model: Transforming NASA Langley’s Role in the Aerospace Technology Ecosystem

NASA’s operating environment is evolving: numerous new emerging technologies are converging to create breakthrough solutions, many nontraditional players are partnering to create those solutions, and a diverse array of new public-private funding models are being employed. A team at NASA’s Langley Research Center (LaRC) developed a model to describe the challenges that must be addressed for an emerging breakthrough technology to penetrate the appropriate market sector, and how all the players in the relevant ecosystem can collaborate to accelerate the market infusion process. This model, which the team dubbed the “Gearamid,” originated from a narrower study to determine what LaRC should do to capitalize on and advance autonomous technology as a “game changer” in the civil aerospace domain. The elements in the original version of the Gearamid depict the various challenges that need to be addressed as an autonomous technology proceeds from initial development to market infusion. In addition, the Gearamid indicates the “actors” best suited to address each challenge element. The study team noted that all challenge elements in the Gearamid must be worked concurrently to assure successful infusion of autonomous solutions. The team also found that NASA expertise naturally positions Agency organizations—including LaRC—to lead contributions that address certain challenge elements and to play a supplemental role in other areas where entities external to NASA are actively working and investing to solve challenges. Furthermore, the team concluded that NASA can play a leadership role in coordinating efforts of the diverse entities across the civil aerospace community. After determining the center’s optimal role in the autonomous technology development ecosystem, the study team then extrapolated the Gearamid model for autonomous technologies into a broader, more general model depicting the challenge elements that must be overcome to develop and infuse any emerging technology. LaRC has embraced the Gearamid model and is using it to drive changes that will transform the center and allow it to function optimally in the evolving landscape. Given LaRC’s successes, the study team suggests that other organizations may also be able to use the Gearamid model to inform future planning/strategy efforts.

Jill M Marlowe↗
NASA NTRS1998

Biomarkers as tracers for life on early earth and Mars

Biomarkers in geological samples are products derived from biochemical (natural product) precursors by reductive and oxidative processes (e.g., cholestanes from cholesterol). Generally, lipids, pigments and biomembranes are preserved best over longer geological times and labile compounds such as amino acids, sugars, etc. are useful biomarkers for recent times. Thus, the detailed characterization of biomarker compositions permits the assessment of the major contributing species of extinct and/or extant life. In the case of the early Earth, work has progressed to elucidate molecular structure and carbon isotropic signals preserved in ancient sedimentary rocks. In addition, the combination of bacterial biochemistry with the organic geochemistry of contemporary and ancient hydrothermal ecosystems permits the modeling of the nature, behavior and preservation potential of primitive microbial communities. This approach uses combined molecular and isotopic analyses to characterize lipids produced by cultured bacteria (representative of ancient strains) and to test a variety of culture conditions which affect their biosynthesis. On considering Mars, the biomarkers from lipids and biopolymers would be expected to be preserved best if life flourished there during its early history (3.5-4 x 10(9) yr ago). Both oxidized and reduced products would be expected. This is based on the inferred occurrence of hydrothermal activity during that time with the concomitant preservation of biochemically-derived organic matter. Both known biomarkers (i.e., as elucidated for early terrestrial samples and for primitive terrestrial microbiota) and novel, potentially unknown compounds should be characterized.

Non-NASA Center↗
NASA NTRS1996

Lipid biomarkers for bacterial ecosystems: studies of cultured organisms, hydrothermal environments and ancient sediments

This paper forms part of our long-term goal of using molecular structure and carbon isotopic signals preserved as hydrocarbons in ancient sediments to improve understanding of the early evolution of Earth's surface environment. We are particularly concerned with biomarkers which are informative about aerobiosis. Here, we combine bacterial biochemistry with the organic geochemistry of contemporary and ancient hydrothermal ecosystems to construct models for the nature, behaviour and preservation potential of primitive microbial communities. We use a combined molecular and isotopic approach to characterize lipids produced by cultured bacteria and test a variety of culture conditions which affect their biosynthesis. This information is then compared with lipid mixtures isolated from contemporary hot springs and evaluated for the kinds of chemical change that would accompany burial and incorporation into the sedimentary record. In this study we have shown that growth temperature does not appear to alter isotopic fractionation within the lipid classes produced by a methanotropic bacterium. We also found that cultured cyanobacteria biosynthesize diagnostic methylalkanes and dimethylalkanes with the latter only made when growing under low pCO2. In an examination of a microbial mat sample from Octopus Spring, Yellowstone National Park (USA), we could readily identify chemical structures with 13C contents which were diagnostic for the phototrophic organisms such as cyanobacteria and Chloroflexus. We could not, however, find molecular evidence for operation of a methane cycle in the particular mat samples we studied.

NASA Discipline Exobiology↗
DOE OSTI2018

ORCHIDEE-PEAT (revision 4596), a model for northern peatland CO 2 , water, and energy fluxes on daily to annual scales

Peatlands store substantial amounts of carbon and are vulnerable to climate change. We present a modified version of the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) land surface model for simulating the hydrology, surface energy, and CO 2 fluxes of peatlands on daily to annual timescales. The model includes a separate soil tile in each 0.5° grid cell, defined from a global peatland map and identified with peat-specific soil hydraulic properties. Runoff from non-peat vegetation within a grid cell containing a fraction of peat is routed to this peat soil tile, which maintains shallow water tables. The water table position separates oxic from anoxic decomposition. The model was evaluated against eddy-covariance (EC) observations from 30 northern peatland sites, with the maximum rate of carboxylation ( V cmax ) being optimized at each site. Regarding short-term day-to-day variations, the model performance was good for gross primary production (GPP) ( r 2 = 0.76; Nash–Sutcliffe modeling efficiency, MEF =0.76) and ecosystem respiration (ER, r 2 = 0.78, MEF = 0.75), with lesser accuracy for latent heat fluxes (LE, r 2 2 exchange (NEE, r 2 = 0.38, MEF = 0.26). Seasonal variations in GPP, ER, NEE, and energy fluxes on monthly scales showed moderate to high r 2 values (0.57–0.86). For spatial across-site gradients of annual mean GPP, ER, NEE, and LE, r 2 values of 0.93, 0.89, 0.27, and 0.71 were achieved, respectively. Water table (WT) variation was not well predicted ( r 2 < 0.1), likely due to the uncertain water input to the peat from surrounding areas. However, the poor performance of WT simulation did not greatly affect predictions of ER and NEE. We found a significant relationship between optimized V cmax and latitude (temperature), which better reflects the spatial gradients of annual NEE than using an average V cmax value

58 GEOSCIENCES↗
DOE OSTI2022

Quantifying the drivers of ecosystem fluxes and water potential across the soil-plant-atmosphere continuum in an arid woodland

Dryland ecosystems occupy a vast swath of the terrestrial land surface and exert a sizeable impact on the cycling of carbon and water globally. These biomes are characterized by tightly coupled carbon and water cycles that respond rapidly to transient pulses in water availability. However, there exist many mechanistic uncertainties regarding the environmental drivers of, and linkages between, plant and ecosystem processes. Thus, drylands are often poorly represented in many vegetation and land surface models. An enhanced understanding of dryland ecosystem function is limited by the lack of long-term, co-located, and frequent measurements of plant and ecosystem processes. At a piñon-juniper woodland in southeastern Utah, USA, we collected a continuous dataset of meteorological conditions, soil water potential from surface to bedrock, tree water potential, and ecosystem carbon and water fluxes from eddy covariance. We found that predawn and midday tree water potential and daily ecosystem fluxes were highly sensitive to fluctuations in soil water availability, particularly in shallower layers, and that daytime variability in atmospheric drivers only loosely controlled these processes. The strong connections between shallow soil water potential, tree water potential, and ecosystem fluxes occurred because of the dominant role of precipitation pulses in driving vegetation activity, as even small pulses of moisture stimulated shallow soil water potential, tree water potential, and evapotranspiration for between 1 and 2 weeks. Carbon fluxes (net ecosystem exchange and gross primary productivity) were sensitive to precipitation pulses for longer, up to 3 weeks. Our results highlight that improved monitoring and sensing of shallow soil moisture can greatly enhance our understanding of dryland ecosystem function. Here, a better mechanistic understanding of the impacts of precipitation pulses is also needed to improve vegetation modeling of dryland ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Business Models for Scaling Demand Flexibility Volume III – Stakeholder ecosystem management challenges, and lessons learned from U.S. programs

Load growth at the grid edge is driving increased attention to the distribution system and its ability to enable customer technology adoption in an affordable and timely manner. Key industry stakeholders, including electric utilities and regulators, can benefit from strategies to manage and balance customer needs with infrastructure investments, such as demand flexibility. This report focuses on demand flexibility—the ability to reduce, shift, shed, generate, or modulate loads in response to building and grid needs—to reduce the need for costly grid upgrades by deferring investment needs and increase system reliability by shifting electricity usage during periods of high risk. Specifically, we focus on the emerging characteristics of business models for demand flexibility as a framework to understand how demand flexibility programs generate value. In this report, we focus on demand flexibility program stakeholder ecosystem management strategies, which provide information on value delivery and describe how program implementers leverage partner capabilities and collaborate to deliver customer and grid benefits. This report discusses the role of stakeholder ecosystem management in demand flexibility programs, identifies existing challenges to effective stakeholder management, and describes lessons learned. This report is part of a series that includes reports on value propositions, customer relationship management strategies, and program life cycle.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Monitoring agroecosystem productivity and phenology at a national scale: A metric assessment framework

Effective measurement of seasonal variations in the timing and amount of production is critical to managing spatially heterogeneous agroecosystems in a changing climate. Although numerous technologies for such measurements are available, their relationships to one another at a continental extent are unknown. Using data collected from across the Long-Term Agroecosystem Research (LTAR) network and other networks, we investigated correlations among key metrics representing primary production, phenology, and carbon fluxes in croplands, grazing lands, and crop-grazing integrated systems across the continental U.S. Metrics we examined included gross primary productivity (GPP) estimated from eddy covariance (EC) towers and modelled from the Landsat satellite, Landsat NDVI, and vegetation greenness (Green Chromatic Coordinate, GCC) from tower-mounted PhenoCams for 2017 and 2018. Overall, our analysis compared production dynamics estimated from three independent ground and remote platforms using data for 34 agricultural sites constituting 51 site-years of co-located time series. Pairwise sensor comparisons across all four metrics revealed stronger correlation and lower root mean square error (RMSE) between end of season (EOS) dates (Pearson R ranged from 0.6 to 0.7 and RMSE from 32.5 to 67.8) than start of season (SOS) dates (0.46 to 0.69 and 40.4 to 66.2). Overall, moderate to high correlations between SOS and EOS metrics complemented one another except at some lower productivity grazing land sites where estimating SOS can be challenging. Growing season length estimates derived from 16-day satellite GPP (179.1 days) were significantly longer than those from PhenoCam G CC (70.4 days, p adj < 0.0001) and EC GPP (79.6 days, p adj < 0.0001). Landscape heterogeneity did not explain differences in SOS and EOS estimates. Annual integrated estimates of productivity from EC GPP and PhenoCam G CC diverged from those estimated by Landsat GPP and NDVI at sites where annual production exceeds 1000 gC/m –2 yr –1 . Based on our results, we developed a “metric assessment framework” that articulates where and how metrics from satellite, eddy covariance and PhenoCams complement, diverge from, or are redundant with one another. The framework was designed to optimize instrumentation selection for monitoring, modeling, and forecasting ecosystem functioning with the ultimate goal of informing decision-making by land managers, policy-makers, and industry leaders working at multiple scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Quantifying pH buffering capacity in acidic, organic-rich Arctic soils: Measurable proxies and implications for soil carbon degradation

Dynamic pH change promoted by biogeochemical reactions in Arctic tundra soils can be a major control on the production and release of CO 2 and CH 4 , which contribute to rising global temperatures. Large quantities of soil organic matter (SOM) in these soils are susceptible to microbial decomposition, leading to pH changes during permafrost thaw. Soil pH buffering capacity (β) modulates the extent of pH change but has not been thoroughly studied and represented in predictive ecosystem scale biogeochemical models in Arctic tundra soils. In this study, we generated titration curves for 21 acidic tundra soils from three Arctic sites across northern Alaska, United States of America. Geochemical and hydrological soil properties were evaluated, and correlations with β were developed. Strong correlations between β and both gravimetric water content (Θ g ) (R 2 = 0.847, p < 0.001) and soil water retention (SWR) (R 2 = 0.849, p = 0.001) indicate that the ability of soil to retain water could be associated with its buffering properties. Correlations between β and soil organic carbon (SOC) and cation exchange capacity (CEC) were also explored, and relationships to SWR are discussed. These correlations were then used with existing soil databases reporting SOC, CEC, and SWR to estimate β across Alaska soils. We further demonstrated the quantitative relationships between β and the simulated rates of biogeochemical reactions and show that lower β leads to higher soil pH and more CH 4 production. Overall, our study provides simple proxies for β in Arctic soils and highlights the importance and implications of representing soil buffering in predictive models, thereby enabling quantitative coupling between pH dynamics associated with biogeochemical reactions. Integrating β into predictive models of Arctic biogeochemical cycling may reduce model uncertainty and further our understanding of permafrost SOM degradation accelerated by warming.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Disentangling the Effects of Temperature, Moisture, and Substrate Availability on Soil CO 2 Efflux

Soil respiration ($R_s$), the largest carbon emission flux in ecosystems, is usually modeled as an empirically parameterized function of temperature, and sometimes water availability. The likely contribution by other factors, such as carbohydrate substrate supply from photosynthesis, has been recognized, but modeling capacity to use this information is limited. Wavelet transformations of the residuals of a seasonal Q 10 temperature response model indicated structure at different temporal scales. In this work, we hypothesize that this indicates the lack of explicit representation of relevant processes in the models. Using cross-spectral analysis, we found that time series of photosynthetically active radiation explained most of the diurnal variation, temperature, explained variability at multiple time scales (diurnal-synoptic), whereas volumetric soil water content correlated with variability in Rs at scales 15-30 days. The results suggest that the time domains of influence for different driving variables of $R_s$ are discrete, and largely nonoverlapping, and represent functional relationships between soil biological activity and its constraints. Analysis of phase angles showed that $R_s$ lagged photosynthetically active radiation by 1.5-3.0 hr. As this time lag was the same in both young and mature trees, with more than fivefold difference in transport distances, we hypothesize that this finding adds to the body of literature that support the pressure-concentration-wave model of carbohydrate availability in plants.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Impoverishing Roots Will Improve Wheat Yield and Profitability Through Increased Water and Nitrogen Use Efficiencies

We report that more than a 60% increase in crop production is required by the 2050's to feed a growing world population. Understanding how plant functional traits and field management affect crop yields has the potential to improve agricultural productivity, minimize economic and environmental losses, and maximize food security. We explored the influence of winter wheat root characteristics and management on winter wheat growth, yield, and profit using a mechanistic and well-tested ecosystem and crop model, ecosys. We applied and further tested ecosys at an agricultural farm growing winter wheat in Ardmore, Oklahoma, United States. The model accurately predicted observed shoot carbon (R 2 = 0.95), soil moisture (R 2 = 0.67), soil temperature (R 2 = 0.91), and yield (percent error = 17%). Numerical optimization experiments were conducted to explore potential improvements of winter wheat yield and profit by modifying root characteristics, including root radius and root:shoot carbon transfer conductance, and fertilizer inputs. Our results show the potential for simultaneously improving winter wheat yields and profits. The optimum conditions were found to be in the range of root radius between 0.1 and 0.3 mm, carbon transfer conductance between 0.004 and 0.01 h -1 , and the currently applied fertilizer rate of 112 kg ha -1 . Under these conditions, improvements in yields and profits of up to approximately 25% and 110%, respectively, were modeled compared to those under baseline root traits. These improvements were achieved by impoverishing root structures, thereby increasing nutrient allocation to grains. Our results also demonstrate and motivate model structures that integrate the complex network of plant physiology, soil nutrient biogeochemistry, hydrology, and management.

54 ENVIRONMENTAL SCIENCES↗